Triple
T26877489
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Megan Hipwell |
E676790
|
entity |
| Predicate | hasTraumaticPastEvent |
P41242
|
FINISHED |
| Object | death of her infant daughter |
—
|
LITERAL 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: death of her infant daughter | Statement: [Megan Hipwell, hasTraumaticPastEvent, death of her infant daughter]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTraumaticPastEvent Context triple: [Megan Hipwell, hasTraumaticPastEvent, death of her infant daughter]
-
A.
hasTragicPast
Indicates that an entity has experienced a significantly sorrowful or traumatic history that influences its present state or characterization.
-
B.
hadEvent
Indicates that an entity experienced, hosted, or was associated with a specific event at some point in time.
-
C.
livedAfterAssault
Indicates that the subject continued to live for some period of time following the occurrence of an assault.
-
D.
hasHistoryOf
Indicates that an entity has a documented prior occurrence or background of a specified condition, event, or state.
-
E.
trauma
chosen
Indicates that an entity has experienced a deeply distressing or harmful event or series of events that cause lasting psychological or emotional impact.
- F. None of above.
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_69eee9bb44988190b6e11652d028bc59 |
completed | April 27, 2026, 4:44 a.m. |
| NER | Named-entity recognition | batch_69f7cec454a88190a9f3bbee2b856636 |
completed | May 3, 2026, 10:40 p.m. |
| PD | Predicate disambiguation | batch_69f7c8977c288190997a892ec5f756ed |
completed | May 3, 2026, 10:13 p.m. |
Created at: April 27, 2026, 5:36 a.m.