Triple
T17102388
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tama area |
E415010
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Komae |
E415011
|
NE FINISHED |
How this triple was built (2 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: Komae | Statement: [Tama area, hasPart, Komae]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Komae Context triple: [Tama area, hasPart, Komae]
-
A.
Komae
chosen
Komae is a small residential city in Tokyo Metropolis, Japan, known for its suburban character and proximity to central Tokyo.
-
B.
Shinkiari
Shinkiari is a town in Pakistan’s Khyber Pakhtunkhwa province, known for its agricultural surroundings and its location along the Karakoram Highway near Mansehra.
-
C.
Kamoenai
Kamoenai is a small coastal village in Hokkaido, Japan, known for its fishing industry and scenic natural surroundings.
-
D.
Takkaze
Takkaze is a river in northern Ethiopia that flows through deep gorges before joining the Atbarah River, ultimately contributing to the Nile basin.
-
E.
Nakoruru
Nakoruru is a popular Samurai Shodown character known as a nature-loving Ainu shrine maiden who fights alongside her hawk and wolf companions.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d886cfc8e88190b05ba466edd35591 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3dc239a088190a776fe0f4361ffc7 |
completed | April 18, 2026, 7:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0139fdda488190a1ca5c7ca875e044 |
completed | May 11, 2026, 2:07 a.m. |
Created at: April 10, 2026, 5:35 a.m.