Triple
T1811214
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hoofddorp railway station |
E40335
|
entity |
| Predicate | stationCode |
P1289
|
FINISHED |
| Object |
Hfd
Hfd is the official station code used to identify Hoofddorp railway station in the Dutch rail network.
|
E202519
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Hfd | Statement: [Hoofddorp railway station, stationCode, Hfd]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hfd Context triple: [Hoofddorp railway station, stationCode, Hfd]
-
A.
HF
HF is the Faculty of Humanities at the University of Oslo, encompassing disciplines such as languages, history, culture, and philosophy.
-
B.
HAD
HAD is the commonly used abbreviation for the Historical Astronomy Division, a group focused on the study and promotion of the history of astronomy.
-
C.
HVF
HVF is a data-focused startup and innovation lab created by entrepreneur Max Levchin to explore and build companies around large-scale data problems.
-
D.
HDH
HDH is the vehicle registration code used on license plates for the German town of Heidenheim an der Brenz.
-
E.
F.D.
F.D. is the standard abbreviation of the Latin title "Fidei Defensor," historically used by English and later British monarchs to denote their role as "Defender of the Faith."
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Hfd Triple: [Hoofddorp railway station, stationCode, Hfd]
Generated description
Hfd is the official station code used to identify Hoofddorp railway station in the Dutch rail network.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hfd Target entity description: Hfd is the official station code used to identify Hoofddorp railway station in the Dutch rail network.
-
A.
HF
HF is the Faculty of Humanities at the University of Oslo, encompassing disciplines such as languages, history, culture, and philosophy.
-
B.
HAD
HAD is the commonly used abbreviation for the Historical Astronomy Division, a group focused on the study and promotion of the history of astronomy.
-
C.
HVF
HVF is a data-focused startup and innovation lab created by entrepreneur Max Levchin to explore and build companies around large-scale data problems.
-
D.
HDH
HDH is the vehicle registration code used on license plates for the German town of Heidenheim an der Brenz.
-
E.
F.D.
F.D. is the standard abbreviation of the Latin title "Fidei Defensor," historically used by English and later British monarchs to denote their role as "Defender of the Faith."
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a88643a3388190a612f2ebe1fb29e7 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aa65c64bc08190b993216890752b46 |
completed | March 6, 2026, 5:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adb5e352d88190839cde25e3c07d95 |
completed | March 8, 2026, 5:46 p.m. |
| NEDg | Description generation | batch_69adb8b6d160819096dc02323049101d |
completed | March 8, 2026, 5:58 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69adb9bafd688190a66a835c6a8163e3 |
completed | March 8, 2026, 6:02 p.m. |
Created at: March 4, 2026, 7:32 p.m.